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library(targets)
library(tidyverse)
library(ggokabeito)
library(easystats)
library(gt)
library(ggfittext)
library(scales)
library(visdat)Analyse der Matoma-HaNS-Daten
1 Setup
1.1 R-Pakete starten
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theme_set(theme_minimal())1.2 Roh-Daten laden und inspizieren (data_all_fact)
JSON-Daten wurden nicht importiert, da offenbar nur redundante Daten enthalten sind.
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tar_load(data_all_fct)1.2.1 Dimension
Der Roh-Datensatz verfügt über
- 160 Zeilen
- 2801 Spalten (Dubletten und Spalten mit Bildern bereits entfernt)
Jede Zeile entspricht einem “Visit”.
1.2.2 Erster Blick
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data_all_fct_head100 <-
data_all_fct %>%
select(1:100) %>%
slice_head(n = 100) Show the code
data_all_fct_head100 %>%
visdat::vis_dat()
1.2.3 Namen (1-100)
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data_all_fct_head100 %>%
names() [1] "file_id" "idvisit"
[3] "visitip" "visitorid"
[5] "fingerprint" "actiondetails_0_type"
[7] "actiondetails_0_url" "actiondetails_0_pageidaction"
[9] "actiondetails_0_idpageview" "actiondetails_0_servertimepretty"
[11] "actiondetails_0_pageid" "actiondetails_0_eventcategory"
[13] "actiondetails_0_eventaction" "actiondetails_0_timestamp"
[15] "actiondetails_0_title" "actiondetails_0_subtitle"
[17] "actiondetails_1_type" "actiondetails_1_url"
[19] "actiondetails_1_pageidaction" "actiondetails_1_idpageview"
[21] "actiondetails_1_servertimepretty" "actiondetails_1_pageid"
[23] "actiondetails_1_timespent" "actiondetails_1_timespentpretty"
[25] "actiondetails_1_pageviewposition" "actiondetails_1_title"
[27] "actiondetails_1_subtitle" "actiondetails_1_timestamp"
[29] "actiondetails_2_type" "actiondetails_2_url"
[31] "actiondetails_2_pageidaction" "actiondetails_2_idpageview"
[33] "actiondetails_2_servertimepretty" "actiondetails_2_pageid"
[35] "actiondetails_2_eventcategory" "actiondetails_2_eventaction"
[37] "actiondetails_2_pageviewposition" "actiondetails_2_timestamp"
[39] "actiondetails_2_title" "actiondetails_2_subtitle"
[41] "actiondetails_3_type" "actiondetails_3_url"
[43] "actiondetails_3_pageidaction" "actiondetails_3_idpageview"
[45] "actiondetails_3_servertimepretty" "actiondetails_3_pageid"
[47] "actiondetails_3_eventcategory" "actiondetails_3_eventaction"
[49] "actiondetails_3_pageviewposition" "actiondetails_3_timestamp"
[51] "actiondetails_3_title" "actiondetails_3_subtitle"
[53] "actiondetails_4_type" "actiondetails_4_url"
[55] "actiondetails_4_pageidaction" "actiondetails_4_idpageview"
[57] "actiondetails_4_servertimepretty" "actiondetails_4_pageid"
[59] "actiondetails_4_timespent" "actiondetails_4_timespentpretty"
[61] "actiondetails_4_pageviewposition" "actiondetails_4_title"
[63] "actiondetails_4_subtitle" "actiondetails_4_timestamp"
[65] "actiondetails_5_type" "actiondetails_5_url"
[67] "actiondetails_5_pageidaction" "actiondetails_5_idpageview"
[69] "actiondetails_5_servertimepretty" "actiondetails_5_pageid"
[71] "actiondetails_5_eventcategory" "actiondetails_5_eventaction"
[73] "actiondetails_5_pageviewposition" "actiondetails_5_timestamp"
[75] "actiondetails_5_title" "actiondetails_5_subtitle"
[77] "actiondetails_6_type" "actiondetails_6_url"
[79] "actiondetails_6_pageidaction" "actiondetails_6_idpageview"
[81] "actiondetails_6_servertimepretty" "actiondetails_6_pageid"
[83] "actiondetails_6_eventcategory" "actiondetails_6_eventaction"
[85] "actiondetails_6_pageviewposition" "actiondetails_6_timestamp"
[87] "actiondetails_6_title" "actiondetails_6_subtitle"
[89] "actiondetails_7_type" "actiondetails_7_url"
[91] "actiondetails_7_pageidaction" "actiondetails_7_idpageview"
[93] "actiondetails_7_servertimepretty" "actiondetails_7_pageid"
[95] "actiondetails_7_timespent" "actiondetails_7_timespentpretty"
[97] "actiondetails_7_pageviewposition" "actiondetails_7_title"
[99] "actiondetails_7_subtitle" "actiondetails_7_timestamp" 1.2.4 Werte der erst 100 Spalten
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data_all_fct_head100 %>%
glimpse()Rows: 100
Columns: 100
$ file_id <fct> matomo_export_2023-10-04.csv, matomo_…
$ idvisit <fct> 6, 5, 4, 2, 3, 1, 25, 26, 24, 22, 23,…
$ visitip <fct> 141.75.152.0, 141.75.168.0, 141.75.15…
$ visitorid <fct> de3a10060112d977, 92f090e11fdbefbe, d…
$ fingerprint <fct> 379a01acad44b5c7, fb231a0ec5c2f0c8, 3…
$ actiondetails_0_type <fct> event, action, action, action, action…
$ actiondetails_0_url <fct> https://hans.th-nuernberg.de/channels…
$ actiondetails_0_pageidaction <fct> 11, 122, 6, 6, 2, 2, 252, 254, 254, 2…
$ actiondetails_0_idpageview <fct> KjY5Mu, tv7t1g, OqlJp6, SqdDri, t8YsD…
$ actiondetails_0_servertimepretty <fct> "Oct 4, 2023 20:27:32", "Oct 4, 2023 …
$ actiondetails_0_pageid <fct> 483, 308, 305, 92, 222, 1, 972, 973, …
$ actiondetails_0_eventcategory <fct> click_button, NA, NA, NA, NA, NA, cli…
$ actiondetails_0_eventaction <fct> Kanäle, NA, NA, NA, NA, NA, Kanäle, N…
$ actiondetails_0_timestamp <fct> 2023-10-04 20:27:32, 2023-10-04 19:45…
$ actiondetails_0_title <fct> Event, HAnS, HAnS, HAnS, HAnS, HAnS, …
$ actiondetails_0_subtitle <fct> "Category: \"\"click_button', Action:…
$ actiondetails_1_type <fct> action, event, event, event, NA, even…
$ actiondetails_1_url <fct> https://hans.th-nuernberg.de/?evalId=…
$ actiondetails_1_pageidaction <fct> 6, 123, 7, 7, NA, 3, 251, NA, 256, 2,…
$ actiondetails_1_idpageview <fct> awdDAu, tv7t1g, OqlJp6, SqdDri, NA, J…
$ actiondetails_1_servertimepretty <fct> "Oct 4, 2023 20:27:33", "Oct 4, 2023 …
$ actiondetails_1_pageid <fct> 484, 309, 306, 93, NA, 2, 974, NA, 97…
$ actiondetails_1_timespent <fct> 22, NA, NA, NA, NA, NA, 23, NA, NA, 6…
$ actiondetails_1_timespentpretty <fct> 22s, NA, NA, NA, NA, NA, 23s, NA, NA,…
$ actiondetails_1_pageviewposition <fct> 1, 1, 1, 1, NA, 1, 1, NA, 1, 2, 1, NA…
$ actiondetails_1_title <fct> HAnS, Event, Event, Event, NA, Event,…
$ actiondetails_1_subtitle <fct> "https://hans.th-nuernberg.de/?evalId…
$ actiondetails_1_timestamp <fct> 2023-10-04 20:27:33, 2023-10-04 19:45…
$ actiondetails_2_type <fct> event, event, action, action, NA, act…
$ actiondetails_2_url <fct> https://hans.th-nuernberg.de/channels…
$ actiondetails_2_pageidaction <fct> 11, 123, 10, 10, NA, 6, 256, NA, 251,…
$ actiondetails_2_idpageview <fct> KjY5Mu, tv7t1g, KjY5Mu, rU2DPV, NA, h…
$ actiondetails_2_servertimepretty <fct> "Oct 4, 2023 20:27:33", "Oct 4, 2023 …
$ actiondetails_2_pageid <fct> 485, 310, 307, 94, NA, 3, 975, NA, 97…
$ actiondetails_2_eventcategory <fct> click_button, click_button, NA, NA, N…
$ actiondetails_2_eventaction <fct> Medien, Abmelden, NA, NA, NA, NA, Med…
$ actiondetails_2_pageviewposition <fct> 1, 1, 2, 2, NA, 2, NA, NA, 2, 3, 1, N…
$ actiondetails_2_timestamp <fct> 2023-10-04 20:27:33, 2023-10-04 19:45…
$ actiondetails_2_title <fct> Event, Event, HAnS, HAnS, NA, HAnS, E…
$ actiondetails_2_subtitle <fct> "Category: \"\"click_button', Action:…
$ actiondetails_3_type <fct> event, action, NA, event, NA, event, …
$ actiondetails_3_url <fct> https://hans.th-nuernberg.de/?evalId=…
$ actiondetails_3_pageidaction <fct> 7, 2, NA, 11, NA, 7, 252, NA, NA, NA,…
$ actiondetails_3_idpageview <fct> awdDAu, UTm1cZ, NA, rU2DPV, NA, hZ9df…
$ actiondetails_3_servertimepretty <fct> "Oct 4, 2023 20:27:44", "Oct 4, 2023 …
$ actiondetails_3_pageid <fct> 486, 311, NA, 95, NA, 4, 976, NA, NA,…
$ actiondetails_3_eventcategory <fct> click_button, NA, NA, click_channelca…
$ actiondetails_3_eventaction <fct> Kanäle, NA, NA, GDI, NA, Kanäle, Kanä…
$ actiondetails_3_pageviewposition <fct> 1, 2, NA, 2, NA, 2, 1, NA, NA, 4, 2, …
$ actiondetails_3_timestamp <fct> 2023-10-04 20:27:44, 2023-10-04 19:45…
$ actiondetails_3_title <fct> Event, HAnS, NA, Event, NA, Event, Ev…
$ actiondetails_3_subtitle <fct> "Category: \"\"click_button', Action:…
$ actiondetails_4_type <fct> action, event, NA, search, NA, action…
$ actiondetails_4_url <fct> https://hans.th-nuernberg.de/channels…
$ actiondetails_4_pageidaction <fct> 10, 3, NA, NA, NA, 10, 254, NA, NA, 2…
$ actiondetails_4_idpageview <fct> ItPeDS, UTm1cZ, NA, oP3co8, NA, YNDCa…
$ actiondetails_4_servertimepretty <fct> "Oct 4, 2023 20:27:44", "Oct 4, 2023 …
$ actiondetails_4_pageid <fct> 487, 312, NA, 96, NA, 5, 977, NA, NA,…
$ actiondetails_4_timespent <fct> 1275, NA, NA, NA, NA, 174, 1433, NA, …
$ actiondetails_4_timespentpretty <fct> 21 min 15s, NA, NA, NA, NA, 2 min 54s…
$ actiondetails_4_pageviewposition <fct> 2, 2, NA, 3, NA, 3, 2, NA, NA, 4, 3, …
$ actiondetails_4_title <fct> HAnS, Event, NA, Site Search, NA, HAn…
$ actiondetails_4_subtitle <fct> "https://hans.th-nuernberg.de/channel…
$ actiondetails_4_timestamp <fct> 2023-10-04 20:27:44, 2023-10-04 19:47…
$ actiondetails_5_type <fct> event, action, NA, action, NA, event,…
$ actiondetails_5_url <fct> https://hans.th-nuernberg.de/channels…
$ actiondetails_5_pageidaction <fct> 11, 6, NA, 16, NA, 11, 254, NA, NA, 2…
$ actiondetails_5_idpageview <fct> ItPeDS, kvsynp, NA, oP3co8, NA, YNDCa…
$ actiondetails_5_servertimepretty <fct> "Oct 4, 2023 20:48:58", "Oct 4, 2023 …
$ actiondetails_5_pageid <fct> 563, 313, NA, 97, NA, 6, 978, NA, NA,…
$ actiondetails_5_eventcategory <fct> click_button, NA, NA, NA, NA, click_b…
$ actiondetails_5_eventaction <fct> Kanäle, NA, NA, NA, NA, Medien, NA, N…
$ actiondetails_5_pageviewposition <fct> 2, 3, NA, 3, NA, 3, 3, NA, NA, 6, 3, …
$ actiondetails_5_timestamp <fct> 2023-10-04 20:48:58, 2023-10-04 19:47…
$ actiondetails_5_title <fct> Event, HAnS, NA, HAnS, NA, Event, HAn…
$ actiondetails_5_subtitle <fct> "Category: \"\"click_button', Action:…
$ actiondetails_6_type <fct> event, search, NA, event, NA, action,…
$ actiondetails_6_url <fct> https://hans.th-nuernberg.de/channels…
$ actiondetails_6_pageidaction <fct> 11, NA, NA, 17, NA, 6, 256, NA, NA, N…
$ actiondetails_6_idpageview <fct> ItPeDS, rM5GmP, NA, oP3co8, NA, IT1VG…
$ actiondetails_6_servertimepretty <fct> "Oct 4, 2023 20:48:59", "Oct 4, 2023 …
$ actiondetails_6_pageid <fct> 564, 314, NA, 98, NA, 7, 979, NA, NA,…
$ actiondetails_6_eventcategory <fct> click_button, NA, NA, click_videocard…
$ actiondetails_6_eventaction <fct> Medien, NA, NA, Kapitel9-5-Maschinens…
$ actiondetails_6_pageviewposition <fct> 2, 4, NA, 4, NA, 4, 3, NA, NA, 7, 3, …
$ actiondetails_6_timestamp <fct> 2023-10-04 20:48:59, 2023-10-04 19:47…
$ actiondetails_6_title <fct> Event, Site Search, NA, Event, NA, HA…
$ actiondetails_6_subtitle <fct> "Category: \"\"click_button', Action:…
$ actiondetails_7_type <fct> action, action, NA, action, NA, event…
$ actiondetails_7_url <fct> https://hans.th-nuernberg.de/?evalId=…
$ actiondetails_7_pageidaction <fct> 6, 16, NA, 58, NA, 7, 255, NA, NA, NA…
$ actiondetails_7_idpageview <fct> Zaut9i, rM5GmP, NA, iyzwzi, NA, IT1VG…
$ actiondetails_7_servertimepretty <fct> "Oct 4, 2023 20:48:59", "Oct 4, 2023 …
$ actiondetails_7_pageid <fct> 565, 315, NA, 99, NA, 8, 980, NA, NA,…
$ actiondetails_7_timespent <fct> 1, 22, NA, 16, NA, NA, 112, NA, NA, N…
$ actiondetails_7_timespentpretty <fct> 1s, 22s, NA, 16s, NA, NA, 1 min 52s, …
$ actiondetails_7_pageviewposition <fct> 3, 4, NA, 5, NA, 4, 4, NA, NA, 8, 3, …
$ actiondetails_7_title <fct> HAnS, HAnS, NA, HAnS, NA, Event, HAnS…
$ actiondetails_7_subtitle <fct> "https://hans.th-nuernberg.de/?evalId…
$ actiondetails_7_timestamp <fct> 2023-10-04 20:48:59, 2023-10-04 19:47…1.2.5 Datensatz data_slim, Zeilen 1-100
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tar_load(data_slim)
data_slim %>%
slice(1:100) |>
gt()| nr | type | value | idvisit |
|---|---|---|---|
| 0 | type | action | 1 |
| 0 | url | https://hans.th-nuernberg.de/login?evalId=none&role=undefined | 1 |
| 0 | timestamp | 2023-10-04 16:19:46 | 1 |
| 0 | title | HAnS | 1 |
| 0 | subtitle | https://hans.th-nuernberg.de/login?evalId=none&role=undefined | 1 |
| 0 | pageloadtime | 0.18s | 1 |
| 0 | pageloadtimemilliseconds | 175 | 1 |
| 1 | type | event | 1 |
| 1 | url | https://hans.th-nuernberg.de/login?evalId=none&role=undefined | 1 |
| 1 | title | Event | 1 |
| 1 | subtitle | Category: ""login', Action: ""success"" | 1 |
| 1 | timestamp | 2023-10-04 16:19:54 | 1 |
| 1 | eventcategory | login | 1 |
| 1 | eventaction | success | 1 |
| 2 | type | action | 1 |
| 2 | url | https://hans.th-nuernberg.de/?evalId=none&role=developer | 1 |
| 2 | timestamp | 2023-10-04 16:19:54 | 1 |
| 2 | title | HAnS | 1 |
| 2 | subtitle | https://hans.th-nuernberg.de/?evalId=none&role=developer | 1 |
| 3 | type | event | 1 |
| 3 | url | https://hans.th-nuernberg.de/?evalId=none&role=developer | 1 |
| 3 | eventcategory | click_button | 1 |
| 3 | eventaction | Kanäle | 1 |
| 3 | timestamp | 2023-10-04 16:19:56 | 1 |
| 3 | title | Event | 1 |
| 3 | subtitle | Category: ""click_button', Action: ""Kanäle"" | 1 |
| 4 | type | action | 1 |
| 4 | url | https://hans.th-nuernberg.de/channels?evalId=none&role=developer | 1 |
| 4 | title | HAnS | 1 |
| 4 | subtitle | https://hans.th-nuernberg.de/channels?evalId=none&role=developer | 1 |
| 4 | timestamp | 2023-10-04 16:19:56 | 1 |
| 5 | type | event | 1 |
| 5 | url | https://hans.th-nuernberg.de/channels?evalId=none&role=developer | 1 |
| 5 | eventcategory | click_button | 1 |
| 5 | eventaction | Medien | 1 |
| 5 | timestamp | 2023-10-04 16:21:23 | 1 |
| 5 | title | Event | 1 |
| 5 | subtitle | Category: ""click_button', Action: ""Medien"" | 1 |
| 6 | type | action | 1 |
| 6 | url | https://hans.th-nuernberg.de/?evalId=none&role=developer | 1 |
| 6 | timestamp | 2023-10-04 16:21:23 | 1 |
| 6 | title | HAnS | 1 |
| 6 | subtitle | https://hans.th-nuernberg.de/?evalId=none&role=developer | 1 |
| 7 | type | event | 1 |
| 7 | url | https://hans.th-nuernberg.de/?evalId=none&role=developer | 1 |
| 7 | title | Event | 1 |
| 7 | subtitle | Category: ""click_button', Action: ""Medien"" | 1 |
| 7 | timestamp | 2023-10-04 16:25:22 | 1 |
| 7 | eventcategory | click_button | 1 |
| 7 | eventaction | Medien | 1 |
| 8 | type | event | 1 |
| 8 | url | https://hans.th-nuernberg.de/?evalId=none&role=developer | 1 |
| 8 | title | Event | 1 |
| 8 | subtitle | Category: ""click_button', Action: ""Kanäle"" | 1 |
| 8 | timestamp | 2023-10-04 16:25:23 | 1 |
| 8 | eventcategory | click_button | 1 |
| 8 | eventaction | Kanäle | 1 |
| 9 | type | action | 1 |
| 9 | url | https://hans.th-nuernberg.de/channels?evalId=none&role=developer | 1 |
| 9 | timestamp | 2023-10-04 16:25:23 | 1 |
| 9 | title | HAnS | 1 |
| 9 | subtitle | https://hans.th-nuernberg.de/channels?evalId=none&role=developer | 1 |
| 10 | type | action | 1 |
| 10 | url | https://hans.th-nuernberg.de/?evalId=none&role=developer | 1 |
| 10 | title | HAnS | 1 |
| 10 | subtitle | https://hans.th-nuernberg.de/?evalId=none&role=developer | 1 |
| 10 | timestamp | 2023-10-04 16:25:24 | 1 |
| 11 | type | event | 1 |
| 11 | url | https://hans.th-nuernberg.de/channels?evalId=none&role=developer | 1 |
| 11 | eventcategory | click_button | 1 |
| 11 | eventaction | Medien | 1 |
| 11 | timestamp | 2023-10-04 16:25:24 | 1 |
| 11 | title | Event | 1 |
| 11 | subtitle | Category: ""click_button', Action: ""Medien"" | 1 |
| 12 | type | action | 1 |
| 12 | url | https://hans.th-nuernberg.de/channels?evalId=none&role=developer | 1 |
| 12 | title | HAnS | 1 |
| 12 | subtitle | https://hans.th-nuernberg.de/channels?evalId=none&role=developer | 1 |
| 12 | timestamp | 2023-10-04 16:25:26 | 1 |
| 13 | type | event | 1 |
| 13 | url | https://hans.th-nuernberg.de/?evalId=none&role=developer | 1 |
| 13 | title | Event | 1 |
| 13 | subtitle | Category: ""click_button', Action: ""Kanäle"" | 1 |
| 13 | timestamp | 2023-10-04 16:25:26 | 1 |
| 13 | eventcategory | click_button | 1 |
| 13 | eventaction | Kanäle | 1 |
| 14 | type | event | 1 |
| 14 | url | https://hans.th-nuernberg.de/channels?evalId=none&role=developer | 1 |
| 14 | eventcategory | click_channelcard | 1 |
| 14 | eventaction | GESOA | 1 |
| 14 | timestamp | 2023-10-04 16:25:27 | 1 |
| 14 | title | Event | 1 |
| 14 | subtitle | Category: ""click_channelcard', Action: ""GESOA"" | 1 |
| 15 | type | search | 1 |
| 15 | title | Site Search | 1 |
| 15 | subtitle | GESOA | 1 |
| 15 | timestamp | 2023-10-04 16:25:27 | 1 |
| 15 | sitesearchkeyword | GESOA | 1 |
| 15 | sitesearchcount | 0 | 1 |
| 16 | type | action | 1 |
1.3 Datensatz nur User
Entfernt man Developer, Admins und Lecturers aus dem Roh-Datensatz so bleiben weniger Zeilen übrig:
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tar_load(data_users_only)- 86 Zeilen
- 2801 Spalten
1.4 Datensatz mit Anzahl der Aktionen pro User
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tar_load(count_action)1.5 Zeitraum
1.5.1 Beginn/Ende der Daten
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tar_load(config)Laut config.yaml ist das aktuelle Semester, d.h. 24-ss.
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tar_load(time_minmax)Show the code
time_minmax |>
summarise(time_min = min(time_min),
time_max = max(time_max)) |>
gt()| time_min | time_max |
|---|---|
| 2023-10-04 16:19:46 | 2023-10-09 22:21:28 |
Diese Statistik wurde auf Basis des Datenobjekts data_slim berechnet.
1.5.2 Days since last visit
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tar_load(time_since_last_visit)
time_since_last_visit <-
time_since_last_visit |>
mutate(dayssincelastvisit = as.numeric(dayssincelastvisit))
time_since_last_visit |>
datawizard::describe_distribution(dayssincelastvisit) |>
knitr::kable()| Variable | Mean | SD | IQR | Min | Max | Skewness | Kurtosis | n | n_Missing |
|---|---|---|---|---|---|---|---|---|---|
| dayssincelastvisit | 1.0125 | 0.1114513 | 0 | 1 | 2 | 8.858956 | 77.44913 | 160 | 0 |
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time_since_last_visit |>
ggplot(aes(x=dayssincelastvisit)) +
geom_density()
1.6 Statistiken
Die folgenden Statistiken beruhen auf dem Datensatz data_slim:
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glimpse(data_slim)Rows: 19,958
Columns: 4
$ nr <int> 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3,…
$ type <fct> type, url, timestamp, title, subtitle, pageloadtime, pageloadt…
$ value <chr> "action", "https://hans.th-nuernberg.de/login?evalId=none&role…
$ idvisit <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…nr fasst die Nummer der Aktion innerhalb eines bestimmten Visits.
1.6.1 Mit allen Daten (den 499er-Daten)
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count_action |>
describe_distribution(nr_max) |>
gt() |>
fmt_number(columns = where(is.numeric),
decimals = 2)| Variable | Mean | SD | IQR | Min | Max | Skewness | Kurtosis | n | n_Missing |
|---|---|---|---|---|---|---|---|---|---|
| nr_max | 33.94 | 48.36 | 46.50 | 1.00 | 211.00 | 1.97 | 3.46 | 86.00 | 0.00 |
nr_max gibt den Maximalwert von nr zurück, sagt also, wie viele Aktionen maximal von einem Visitor ausgeführt wurden.
Betrachtet man die Anzahl der Aktionen pro Visitor näher, so fällt auf, dass der Maximalwert (499) sehr häufig vorkommt:
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count_action |>
count(nr_max) |>
ggplot(aes(x = nr_max, y = n)) +
geom_col()
Hier noch in einer anderen Darstellung:
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count_action |>
count(nr_max) |>
ggplot(aes(x = nr_max, y = n)) +
geom_point()
Der Maximalwert ist einfach auffällig häufig:
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count_action |>
count(nr_max == 499) |>
gt()| nr_max == 499 | n |
|---|---|
| FALSE | 86 |
Es erscheint plausibel, dass der Maximalwert alle “gekappten” (zensierten, abgeschnittenen) Werte fasst, also viele Werte, die eigentlich größer wären (aber dann zensiert wurden).
1.6.2 Nur Visitors, für die weniger als 500 Aktionen protokolliert sind
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count_action2 <-
count_action |>
filter(nr_max != 499)
count_action2 |>
describe_distribution(nr_max) |>
gt() |>
fmt_number(columns = where(is.numeric),
decimals = 2)| Variable | Mean | SD | IQR | Min | Max | Skewness | Kurtosis | n | n_Missing |
|---|---|---|---|---|---|---|---|---|---|
| nr_max | 33.94 | 48.36 | 46.50 | 1.00 | 211.00 | 1.97 | 3.46 | 86.00 | 0.00 |
1.7 Verteilung
1.7.1 Mit den 499er-Daten
Show the code
count_action_avg = mean(count_action$nr_max)
count_action_sd = sd(count_action$nr_max)
count_action |>
ggplot() +
geom_histogram(aes(x = nr_max)) +
labs(x = "Anzahl von Aktionen pro Visit",
y = "n",
caption = "Der vertikale Strich zeigt den Mittelwert; der horizontale die SD") +
theme_minimal() +
geom_vline(xintercept = count_action_avg,
color = palette_okabe_ito()[1]) +
geom_segment(x = count_action_avg-count_action_sd,
y = 0,
xend = count_action_avg + count_action_sd,
yend = 0,
color = palette_okabe_ito()[2],
size = 2) +
annotate("label", x = count_action_avg, y = 1500, label = "MW") +
annotate("label", x = count_action_avg + count_action_sd, y = 0, label = "SD")
Show the code
#geom_label(aes(x = count_action_avg), y = 1, label = "Mean")- Mittelwert der Aktionen pro Visit: 33.94.
- SD der Aktionen pro Visit: 48.36.
1.7.2 Ohne 499er-Daten
Show the code
count_action_avg2 = mean(count_action2$nr_max)
count_action_sd2 = sd(count_action2$nr_max)
count_action2 |>
ggplot() +
geom_histogram(aes(x = nr_max)) +
labs(x = "Anzahl von Aktionen pro Visit",
y = "n",
title = "Verteilung der User-Aktionen pro Visit",
caption = "Der vertikale Strich zeigt den Mittelwert; der horizontale die SD") +
theme_minimal() +
geom_vline(xintercept = count_action_avg2,
color = palette_okabe_ito()[1]) +
geom_segment(x = count_action_avg-count_action_sd2,
y = 0,
xend = count_action_avg2 + count_action_sd2,
yend = 0,
color = palette_okabe_ito()[2],
size = 2) +
annotate("label", x = count_action_avg2, y = 1500, label = "MW", vjust = "top") +
annotate("label", x = count_action_avg2 + count_action_sd2, y = 0, label = "SD", vjust = "bottom")
Show the code
#geom_label(aes(x = count_action_avg), y = 1, label = "Mean")- Mittelwert der Aktionen pro Visit: 33.94.
- SD der Aktionen pro Visit: 48.36.
2 Zeit pro Visit
Die Visit-Zeit wurde auf 600 Min. trunkiert/begrenzt.
Show the code
tar_load(time_spent)
tar_load(time_duration)
time_spent <-
time_spent |>
mutate(t_min = as.numeric(time_diff, units = "mins")) |>
filter(t_min < 600)2.1 Verweildauer-Statistiken in Sekunden
Show the code
time_spent |>
summarise(
mean_time_diff = round(mean(time_diff), 2),
sd_time_diff = sd(time_diff),
min_time_diff = min(time_diff),
max_time_diff = max(time_diff)
) |>
summarise(
mean_time_diff_avg = mean(mean_time_diff),
sd_time_diff_avg = mean(sd_time_diff, na.rm = TRUE),
min_time_diff_avg = mean(min_time_diff),
max_time_diff_avg = mean(max_time_diff)
) |>
gt() |>
fmt_number(columns = everything(),
decimals = 2)| mean_time_diff_avg | sd_time_diff_avg | min_time_diff_avg | max_time_diff_avg |
|---|---|---|---|
| 169.23 | 0.00 | 169.23 | 169.23 |
Show the code
tar_load(time_duration)
time_duration |>
summarise(duration_sec_avg = mean(visitduration_sec, na.rm = TRUE)) |>
mutate(duration_min_avg = duration_sec_avg / 60) duration_sec_avg duration_min_avg
1 961.3875 16.023132.2 Verweildauer-Statistiken in Minuten
Show the code
time_spent |>
summarise(
mean_t_min = mean(t_min),
sd_t_min = sd(t_min),
min_t_min = min(t_min),
max_t_min = max(t_min)
) |>
summarise(
mean_t_min_avg = mean(mean_t_min),
sd_t_min_avg = mean(sd_t_min, na.rm = TRUE),
min_t_min_avg = mean(min_t_min),
max_t_min_avg = mean(max_t_min)
) |>
gt() |>
fmt_number(columns = everything(),
decimals = 2)| mean_t_min_avg | sd_t_min_avg | min_t_min_avg | max_t_min_avg |
|---|---|---|---|
| 169.23 | 0.00 | 169.23 | 169.23 |
2.3 Visualisierung der Verweildauer
2.3.1 bins=20
Show the code
time_spent |>
ggplot(aes(x = t_min)) +
geom_histogram() +
scale_x_time() +
theme_minimal() +
labs(y = "n",
x = "Verweildauer in HaNS pro Visit in Minuten")
2.3.2 bins=100
Show the code
time_spent |>
ggplot(aes(x = t_min)) +
geom_histogram(binwidth = 5) +
theme_minimal() +
labs(y = "n",
x = "Verweildauer in Minuten",
title = "Verweildauer in HaNS pro Visit",
caption = "binwidth = 5 Min.")
2.3.3 Zeitdauer begrenzt auf 1-120 Min.
Show the code
time_spent2 <-
time_spent |>
filter(t_min > 1, t_min < 120)
time_spent2 |>
ggplot(aes(x = t_min)) +
geom_histogram(binwidth = 10) +
theme_minimal() +
labs(y = "n",
x = "Verweildauer in HaNS pro Visit in Minuten",
title = "Verweildauer begrenzt auf 1-120 Minuten",
caption = "bindwidth = 10 Min.")
3 Was machen die User?
Show the code
tar_load(count_action_type)3.1 Häufigkeiten
3.1.1 Nach Kategorien
Show the code
count_action_type |>
count(category, sort = TRUE) |>
mutate(prop = round(n/sum(n), 2)) |>
gt()| category | n | prop |
|---|---|---|
| video | 1895 | 0.63 |
| visit_page | 417 | 0.14 |
| click_slideChange | 396 | 0.13 |
| NA | 86 | 0.03 |
| click_topic | 51 | 0.02 |
| login | 49 | 0.02 |
| Search Results Count | 43 | 0.01 |
| in_media_search | 26 | 0.01 |
| Medien | 23 | 0.01 |
| GESOA | 9 | 0.00 |
| Kanäle | 9 | 0.00 |
| click_channelcard | 1 | 0.00 |
3.1.2 eventcategory
Was machen die Visitors eigentlich? Und wie oft?
Show the code
data_slim |>
filter(type == "eventcategory") |>
count(value, sort = TRUE) |>
gt()| value | n |
|---|---|
| videoplayer_click | 1719 |
| click_slideChange | 396 |
| click_button | 50 |
| login | 49 |
| click_start_resize | 33 |
| click_stop_resize | 33 |
| click_videocard | 31 |
| click_topic_details | 30 |
| click_topic_position_using_image | 20 |
| click_transcript_word | 20 |
| in_media_search | 18 |
| logout | 10 |
| click_videocard_search_lecturer | 9 |
| in_media_search_results | 5 |
| click_channelcard | 4 |
| eval | 4 |
| click_in_media_search_results | 3 |
| click_toggle | 2 |
| click_videocard_search_course_acronym | 2 |
| click_topic_position_using_link | 1 |
| userRole | 1 |
3.2 Verteilung
3.2.1 Rohwerte
Show the code
count_action_type |>
count(category, sort = TRUE) |>
ggplot(aes(y = reorder(category, n), x = n)) +
geom_col() +
geom_bar_text() +
labs(
x = "User-Aktion",
y = "Aktion",
title = "Anzahl der User-Aktionen nach Kategorie"
) +
theme_minimal() +
scale_x_continuous(labels = scales::comma)
3.2.2 Log-Skalierung
Show the code
count_action_type |>
count(category, sort = TRUE) |>
ggplot(aes(y = reorder(category, n), x = n)) +
geom_col() +
geom_bar_text() +
labs(
x = "Anazhl der User-Aktionen",
y = "Aktion",
title = "Anzahl der User-Aktionen nach Kategorie",
caption = "Log10-Skala"
) +
theme_minimal() +
scale_x_log10()
4 An welchen Tagen und zu welcher Zeit kommen die User zu HaNS?
4.1 Setup
Show the code
tar_load(time_visit_wday)Show the code
# Define a vector with the names of the days of the week
# Note: Adjust the start of the week (Sunday or Monday) as per your requirement
days_of_week <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")
# Replace numbers with day names
time_visit_wday$dow2 <- factor(days_of_week[time_visit_wday$dow],
levels = days_of_week)4.2 HaNS-Login nach Uhrzeit
Show the code
time_visit_wday |>
as_tibble() |>
count(hour) |>
mutate(prop = n/sum(n)) |>
ggplot(aes(x = hour, y = prop)) +
geom_col() +
theme_minimal() +
labs(
title = "HaNS-Nutzer sind keine Frühaufsteher",
x = "Uhrzeit",
y = "Anteil"
)
Show the code
# coord_polar()Show the code
time_visit_wday |>
as_tibble() |>
count(hour) |>
mutate(prop = n/sum(n)) |>
ggplot(aes(x = hour, y = prop)) +
geom_col() +
theme_minimal() +
coord_polar()
4.3 Verteilung der HaNS-Besuche nach Wochentagen
Show the code
time_visit_wday |>
as_tibble() |>
count(dow2) |>
mutate(prop = n/sum(n)) |>
ggplot(aes(x = dow2, y = prop)) +
geom_col() +
theme_minimal() +
labs(title = "Verteilung der HaNS-Logins nach Wochentagen",
x = "Wochentag",
y = "Anteil")
Show the code
# coord_polar()Show the code
time_visit_wday |>
as_tibble() |>
count(dow2) |>
mutate(prop = n/sum(n)) |>
ggplot(aes(x = dow2, y = prop)) +
geom_col() +
theme_minimal() +
labs(title = "Verteilung der HaNS-Logins nach Wochentagen",
x = "Wochentag",
y = "Anteil") +
coord_polar()
4.3.1 HaNS-Login nach Wochentagen Uhrzeit
Show the code
time_visit_wday |>
as_tibble() |>
count(dow2, hour) |>
group_by(dow2) |>
mutate(prop = n/sum(n)) |>
ggplot(aes(x = hour, y = prop)) +
geom_col() +
facet_wrap(~ dow2) +
theme_minimal() +
labs(title = "Verteilung der HaNS-Logins nach Wochentagen und Uhrzeiten",
x = "Wochentag",
y = "Anteil")
Show the code
# coord_polar()Show the code
time_visit_wday |>
as_tibble() |>
count(dow2, hour) |>
group_by(dow2) |>
mutate(prop = n/sum(n)) |>
ggplot(aes(x = hour, y = prop)) +
geom_col() +
facet_wrap(~ dow2) +
theme_minimal() +
labs(title = "Verteilung der HaNS-Logins nach Wochentagen und Uhrzeiten",
x = "Wochentag",
y = "Anteil") +
coord_polar()
4.4 Anzahl der Visits nach Datum (Tagen) und Uhrzeit (bin2d)
Show the code
time2 <-
time_visit_wday |>
ungroup() |>
mutate(date = as.Date(date_time))
time2 |>
ggplot(aes(x = date, y = hour)) +
geom_bin2d(binwidth = c(1, 1)) + # (1 day, 1 hour)
scale_x_date(date_breaks = "1 month") +
theme(legend.position = "bottom") +
scale_fill_viridis_c() +
labs(caption = "Each x-bin maps to one week")
4.5 Anzahl der Visits nach Datum (Wochen) und Uhrzeit (bin2d)
Show the code
time2 |>
ggplot(aes(x = date, y = hour)) +
geom_bin2d(binwidth = c(7, 1)) + # 1 week, 1 hour
scale_x_date(date_breaks = "1 week", date_labels = "%W") +
theme(legend.position = "bottom") +
scale_fill_viridis_c() +
labs(x = "Week number in 2023/2024",
caption = "Each x-bin maps to one week")
4.6 Anzahl der Visits nach Datum (Wochen) und Wochentag (bin2d)
Show the code
time2 |>
ggplot(aes(x = date, y = dow)) +
geom_bin2d(binwidth = c(7, 1)) + # 1 week, 1 hour
scale_x_date(date_breaks = "1 week", date_labels = "%W") +
theme(legend.position = "bottom") +
scale_fill_viridis_c() +
labs(x = "Week number in 2023/2024",
caption = "Each x-bin maps to one week",
y = "Day of Week") +
scale_y_continuous(breaks = 1:7)
5 KI-Gebrauch
5.1 Interaktion mit dem LLM
5.1.1 Art und Anzahl der Interaktionen mit dem LLM
Show the code
data_slim |>
filter(type == "eventcategory") |>
filter(str_detect(value, "llm")) |>
count(value, sort = TRUE) |>
mutate(prop = n / round(sum(n), 2)) |>
gt()| value | n | prop |
|---|
5.1.2 Anteil Visitors, die mit dem LLM interagieren
Show the code
data_slim |>
mutate(has_llm = str_detect(value, "llm")) |>
group_by(idvisit) |>
summarise(llm_used_during_visit = any(has_llm == TRUE)) |>
count(llm_used_during_visit) |>
mutate(prop = round(n /sum(n), 2)) |>
gt()| llm_used_during_visit | n | prop |
|---|---|---|
| FALSE | 86 | 1 |
5.1.3 … Im Zeitverlauf
Show the code
tar_load(idvisit_has_llm)
idvisit_has_llm |>
count(year_month, uses_llm) |>
ungroup() |>
group_by(year_month) |>
mutate(prop = round(n/sum(n), 2)) |>
gt()| uses_llm | n | prop |
|---|---|---|
| 2023-10 | ||
| FALSE | 95 | 1 |
Show the code
idvisit_has_llm |>
count(year_month, uses_llm) |>
ungroup() |>
group_by(year_month) |>
mutate(prop = n/sum(n)) |>
ggplot(aes(x = year_month, y = prop, color = uses_llm, groups = uses_llm)) +
geom_point() +
geom_line(aes(group = uses_llm)) +
labs(title = "Visitors, die mit dem LLM interagieren im Zeitverlauf (Anteile)")
Show the code
idvisit_has_llm |>
count(year_month, uses_llm) |>
ungroup() |>
group_by(year_month) |>
ggplot(aes(x = year_month, y = n, color = uses_llm, groups = uses_llm)) +
geom_point() +
geom_line(aes(group = uses_llm)) +
labs(title = "Visitors, die mit dem LLM interagieren im Zeitverlauf (Anzahl)")
5.2 Klick auf ein Wort im Transkript
Show the code
tar_load(data_slim)Show the code
data_slim |>
filter(type == "subtitle") |>
filter(!is.na(value) & value != "") |>
count(click_transcript_word = str_detect(value, "click_transcript_word")) |>
mutate(prop = round(n/sum(n), 2)) |>
gt()| click_transcript_word | n | prop |
|---|---|---|
| FALSE | 2985 | 0.99 |
| TRUE | 20 | 0.01 |
5.3 KI-Aktionen
5.3.1 Insgesamt (ganzer Zeitraum)
Show the code
tar_load(data_long)5.3.1.1 Im Detail
Show the code
ai_actions_count <-
data_long |>
filter(str_detect(value, "transcript")) |>
count(value)
ai_actions_count |>
gt()| value | n |
|---|---|
| Category: ""click_transcript_word', Action: ""word: Bei - pos: 346.3 - index: 765"" | 1 |
| Category: ""click_transcript_word', Action: ""word: Diesen - pos: 453.9 - index: 1009"" | 1 |
| Category: ""click_transcript_word', Action: ""word: Fall - pos: 540.22 - index: 1233"" | 1 |
| Category: ""click_transcript_word', Action: ""word: Ja, - pos: 426.74 - index: 944"" | 1 |
| Category: ""click_transcript_word', Action: ""word: Mayer - pos: 1140.58 - index: 2628"" | 2 |
| Category: ""click_transcript_word', Action: ""word: Professionen - pos: 356.74 - index: 797"" | 1 |
| Category: ""click_transcript_word', Action: ""word: Promotionsrecht, - pos: 823.28 - index: 1864"" | 1 |
| Category: ""click_transcript_word', Action: ""word: Schauen - pos: 498.2 - index: 1124"" | 1 |
| Category: ""click_transcript_word', Action: ""word: Und - pos: 979.54 - index: 2229"" | 1 |
| Category: ""click_transcript_word', Action: ""word: Wenn - pos: 330.04 - index: 731"" | 1 |
| Category: ""click_transcript_word', Action: ""word: Wir - pos: 659.98 - index: 1501"" | 1 |
| Category: ""click_transcript_word', Action: ""word: die - pos: 1731.44 - index: 3895"" | 1 |
| Category: ""click_transcript_word', Action: ""word: gibt - pos: 473.12 - index: 1064"" | 1 |
| Category: ""click_transcript_word', Action: ""word: hat, - pos: 382.66 - index: 850"" | 1 |
| Category: ""click_transcript_word', Action: ""word: keinerlei - pos: 1740.08 - index: 3917"" | 1 |
| Category: ""click_transcript_word', Action: ""word: kleinen - pos: 972.18 - index: 2213"" | 1 |
| Category: ""click_transcript_word', Action: ""word: macht - pos: 126.54 - index: 260"" | 1 |
| Category: ""click_transcript_word', Action: ""word: sind - pos: 1098.82 - index: 2536"" | 1 |
| Category: ""click_transcript_word', Action: ""word: wissenschaftlichen - pos: 415.98 - index: 920"" | 1 |
| click_transcript_word | 20 |
5.3.1.2 Zusammengefasst nach “click transcript word”
Show the code
ai_actions_count |>
mutate(value = case_when(
str_detect(value, "click_transcript_word.*") ~ "click transcript word",
TRUE ~ value
)) |>
count(value, sort = TRUE) |>
gt()| value | n |
|---|---|
| click transcript word | 20 |
5.3.2 KI-Klicks pro Monat
Show the code
tar_load(ai_transcript_clicks_per_month)Show the code
ai_transcript_clicks_per_month |>
count(year_month, clicks_transcript_any) |>
ungroup() |>
group_by(year_month) |>
mutate(prop = round(n/sum(n), 2)) |>
gt()| clicks_transcript_any | n | prop |
|---|---|---|
| 2023-10 | ||
| FALSE | 87 | 0.92 |
| TRUE | 8 | 0.08 |